"""
Step 09: 可视化

输入: 01_clean.xlsx, 02_demand.xlsx, 03_allocate.xlsx, 04_metrics.xlsx,
      05_schedule.xlsx, 08_sensitivity.xlsx
输出: figures/*.png

功能:
1. Fig.1: 站点地图 (需求大小 + 访问频次)
2. Fig.2: 需求修正对比 (修正前后μ)
3. Fig.3: 频次分配分布 (k直方图)
4. Fig.4: 有效性-公平性权衡 (E-F散点图)
5. Fig.5: 日历热力图 (全年排程)
6. Fig.6: 访问间隔箱线图
7. Fig.7: 敏感性分析 (参数-指标折线图)
"""

from __future__ import annotations

import os
import pandas as pd
import numpy as np
from pathlib import Path
import warnings
warnings.filterwarnings('ignore')

# 避免 matplotlib/fontconfig 在不可写目录建缓存导致的告警/性能问题
os.environ.setdefault("MPLCONFIGDIR", str((Path(__file__).parent / ".mpl_cache").resolve()))

import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import json

# 设置中文字体 (macOS)
plt.rcParams['font.sans-serif'] = ['Arial Unicode MS', 'SimHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False

# 论文风格主题（参考 tu.png：柔和蓝/绿/紫/橙，浅网格，圆角图例框）
TU = {
    "blue_light": "#a0b0d8",
    "blue_mid": "#7880b0",
    "blue_dark": "#384870",
    "teal": "#487890",
    "green": "#88b0a0",
    "olive": "#808860",
    "mauve": "#a080a0",
    "taupe": "#b09890",
    "orange": "#d0a080",
    "gray": "#a0a0a0",
    "grid": "#e8e8e8",
    "text": "#2b2b2b",
}


def _cmap_k() -> LinearSegmentedColormap:
    return LinearSegmentedColormap.from_list("tu_k", [TU["blue_light"], TU["blue_mid"], TU["blue_dark"]])


def _cmap_heat() -> LinearSegmentedColormap:
    return LinearSegmentedColormap.from_list("tu_heat", ["#f3f4f6", TU["green"], TU["teal"], TU["blue_dark"]])


def apply_tu_theme() -> None:
    plt.rcParams.update(
        {
            "figure.facecolor": "white",
            "axes.facecolor": "white",
            "axes.edgecolor": TU["gray"],
            "axes.labelcolor": TU["text"],
            "xtick.color": TU["text"],
            "ytick.color": TU["text"],
            "axes.titlecolor": TU["blue_dark"],
            "axes.titleweight": "bold",
            "axes.grid": True,
            "grid.color": TU["grid"],
            "grid.linewidth": 0.8,
            "grid.alpha": 1.0,
            "axes.spines.top": False,
            "axes.spines.right": False,
            "legend.frameon": True,
            "legend.fancybox": True,
            "legend.framealpha": 0.92,
            "legend.edgecolor": TU["gray"],
            "legend.facecolor": "#f8f8f8",
        }
    )


def style_axes(ax, *, grid_axis: str = "both") -> None:
    ax.grid(True, axis=grid_axis, linestyle="-", alpha=1.0)
    ax.tick_params(width=0.8)
    for side in ("left", "bottom"):
        ax.spines[side].set_color(TU["gray"])
        ax.spines[side].set_linewidth(0.9)

# 路径配置
BASE_PATH = Path(__file__).parent
FIGURES_PATH = BASE_PATH / "figures"
FIGURES_PATH.mkdir(exist_ok=True)

# 输入文件
CLEAN_PATH = BASE_PATH / "01_clean.xlsx"
DEMAND_PATH = BASE_PATH / "02_demand.xlsx"
ALLOCATE_PATH = BASE_PATH / "03_allocate.xlsx"
METRICS_PATH = BASE_PATH / "04_metrics.xlsx"
SCHEDULE_PATH = BASE_PATH / "05_schedule.xlsx"
SENSITIVITY_PATH = BASE_PATH / "08_sensitivity.xlsx"


def export_fig1_points_js() -> Path:
    """
    Export `fig1_points.js` used by `task1/fig1_carto.html`.
    Data source: `task1/03_allocate.xlsx`.
    """
    df = pd.read_excel(ALLOCATE_PATH).copy()
    df["site_id"] = df["site_id"].astype(int)
    df["k"] = df["k"].astype(int)

    points = []
    for _, r in df.iterrows():
        points.append(
            {
                "site_id": int(r["site_id"]),
                "site_name": str(r["site_name"]),
                "lat": float(r["lat"]),
                "lng": float(r["lon"]),
                "mu": float(r["mu"]),
                "k": int(r["k"]),
                "visits_2019": int(r["visits_2019"]),
            }
        )

    out = BASE_PATH / "fig1_points.js"
    payload = (
        "// Auto-generated from `task1/03_allocate.xlsx` (site_id, site_name, lat, lon, mu, k, visits_2019)\n"
        "// Usage: include this file before `fig1_carto.html` rendering script.\n"
        f"window.FIG1_POINTS = {json.dumps(points, ensure_ascii=False, separators=(',', ':'))};\n"
    )
    out.write_text(payload, encoding="utf-8")
    return out


def fig1_site_map():
    """Fig.1: 站点地图"""
    print("  生成 Fig.1: 站点地图...")
    df = pd.read_excel(ALLOCATE_PATH)
    fig, ax = plt.subplots(figsize=(12, 10))
    
    # 1. 设置地理纵横比 (核心修改)
    avg_lat = df['lat'].mean()
    # 修正经纬度比例：y轴与x轴的比例
    ax.set_aspect(1 / np.cos(np.radians(avg_lat)), adjustable='box')

    # 散点图: 大小=μ, 颜色=k
    scatter = ax.scatter(
        df['lon'], df['lat'],
        s=df['mu'] * 0.8,
        c=df['k'],
        cmap=_cmap_k(),
        alpha=0.85,
        edgecolors='white',
        linewidths=0.7
    )
    
    # ... (标注高需求站点的代码保持不变) ...

    # 颜色条
    cbar = plt.colorbar(scatter, ax=ax, shrink=0.7) # 略微调小一点，防止挤压地图
    cbar.set_label('Visit Frequency (k)', fontsize=12, color=TU["text"])
    
    # ... (图例和标签代码保持不变) ...
    
    # ax.set_title('Fig.1: Site Map (Demand μ & Visit Frequency k)', fontsize=14, fontweight='bold')
    ax.set_xlabel('Longitude', fontsize=12)
    ax.set_ylabel('Latitude', fontsize=12)
    style_axes(ax)
    plt.tight_layout()
    plt.savefig(FIGURES_PATH / 'fig1_site_map.png', dpi=150, bbox_inches='tight')
    plt.close()


def fig2_demand_correction():
    """Fig.2: 需求修正对比"""
    print("  生成 Fig.2: 需求修正对比...")

    df = pd.read_excel(DEMAND_PATH)

    # 只显示被修正的站点
    corrected = df[df['is_corrected']].copy()
    corrected = corrected.sort_values('mu', ascending=False)

    fig, ax = plt.subplots(figsize=(10, 6))

    x = np.arange(len(corrected))
    width = 0.35

    bars1 = ax.bar(x - width/2, corrected['mu'], width, label='Original μ', color=TU["teal"], alpha=0.85, edgecolor="white", linewidth=0.6)
    bars2 = ax.bar(x + width/2, corrected['mu_tilde'], width, label='Corrected μ̃', color=TU["green"], alpha=0.85, edgecolor="white", linewidth=0.6)

    # 添加数值标签
    for bar, val in zip(bars1, corrected['mu']):
        ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 5, f'{val:.0f}',
                ha='center', va='bottom', fontsize=9)
    for bar, val in zip(bars2, corrected['mu_tilde']):
        ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 5, f'{val:.0f}',
                ha='center', va='bottom', fontsize=9, color=TU["green"])

    # 添加p_trunc标注
    for i, (_, row) in enumerate(corrected.iterrows()):
        ax.text(i, max(row['mu'], row['mu_tilde']) + 25,
                f"p={row['p_trunc']:.2%}",
                ha='center', fontsize=8, style='italic')

    ax.set_xlabel('Site', fontsize=12)
    ax.set_ylabel('Demand per Visit', fontsize=12)
    # ax.set_title('Fig.2: Truncation Correction for High-Demand Sites', fontsize=14, fontweight='bold')
    ax.set_xticks(x)
    ax.set_xticklabels([name[:20] for name in corrected['site_name']], rotation=30, ha='right', fontsize=9)
    ax.legend(fontsize=10)
    ax.set_ylim(0, corrected['mu_tilde'].max() * 1.2)
    style_axes(ax, grid_axis="y")

    plt.tight_layout()
    plt.savefig(FIGURES_PATH / 'fig2_demand_correction.png', dpi=150, bbox_inches='tight')
    plt.close()


def fig3_k_distribution():
    """Fig.3: 频次分配分布"""
    print("  生成 Fig.3: 频次分配分布...")

    df = pd.read_excel(ALLOCATE_PATH)

    fig, axes = plt.subplots(1, 2, figsize=(14, 5))

    # 左图: k的直方图
    ax1 = axes[0]
    bins = np.arange(df['k'].min() - 0.5, df['k'].max() + 1.5, 1)
    ax1.hist(df['k'], bins=bins, color=TU["blue_mid"], edgecolor="white", alpha=0.85)
    ax1.axvline(df['k'].mean(), color=TU["mauve"], linestyle='--', linewidth=2, label=f'Mean = {df["k"].mean():.1f}')
    ax1.axvline(df['k'].median(), color=TU["olive"], linestyle=':', linewidth=2, label=f'Median = {df["k"].median():.0f}')
    ax1.set_xlabel('Visit Frequency (k)', fontsize=12)
    ax1.set_ylabel('Number of Sites', fontsize=12)
    ax1.set_title('(a) Distribution of Visit Frequencies', fontsize=12)
    ax1.legend(fontsize=10)
    style_axes(ax1)

    # 右图: k与μ̃的关系
    ax2 = axes[1]
    # mu_tilde already in allocate file
    ax2.scatter(df['mu_tilde'], df['k'], alpha=0.75, s=65, c=TU["green"], edgecolors='white', linewidths=0.7)

    # 拟合线
    z = np.polyfit(df['mu_tilde'], df['k'], 1)
    p = np.poly1d(z)
    x_fit = np.linspace(df['mu_tilde'].min(), df['mu_tilde'].max(), 100)
    ax2.plot(x_fit, p(x_fit), linestyle='--', color=TU["blue_dark"], linewidth=2, label=f'Linear fit: k = {z[0]:.3f}μ̃ + {z[1]:.1f}')

    # 相关系数
    corr = np.corrcoef(df['mu_tilde'], df['k'])[0, 1]
    ax2.text(0.05, 0.95, f'r = {corr:.4f}', transform=ax2.transAxes, fontsize=11,
             verticalalignment='top', bbox=dict(boxstyle='round', facecolor="#f3f4f6", edgecolor=TU["gray"], alpha=0.95))

    ax2.set_xlabel('Corrected Demand (μ̃)', fontsize=12)
    ax2.set_ylabel('Visit Frequency (k)', fontsize=12)
    ax2.set_title('(b) k vs μ̃ (Proportionality Check)', fontsize=12)
    ax2.legend(fontsize=10)
    style_axes(ax2)

    # plt.suptitle('Fig.3: Visit Frequency Allocation Analysis', fontsize=14, fontweight='bold', y=1.02)
    plt.tight_layout()
    plt.savefig(FIGURES_PATH / 'fig3_k_distribution.png', dpi=150, bbox_inches='tight')
    plt.close()


def fig4_efficiency_fairness():
    """Fig.4: 有效性-公平性权衡"""
    print("  生成 Fig.4: 有效性-公平性权衡...")

    df = pd.read_excel(METRICS_PATH, sheet_name='metrics_summary')

    fig, ax = plt.subplots(figsize=(8, 4.96))

    # 绘制所有方案（固定4个点，采用显式样式，便于控制图例与标注）
    from matplotlib.lines import Line2D

    method_styles = [
        {"key": "Recommended", "color": TU["blue_dark"], "marker": "o"},
        {"key": "Baseline 1", "color": TU["mauve"], "marker": "s"},
        {"key": "Baseline 2", "color": TU["olive"], "marker": "^"},
        {"key": "Baseline 3", "color": TU["orange"], "marker": "D"},
    ]

    def _style_for(method: str):
        for s in method_styles:
            if str(method).startswith(s["key"]):
                return s
        return {"color": TU["gray"], "marker": "o"}

    # 标注偏移：避免右上两个点互相遮挡；同时避免“点覆盖字”
    label_offsets = {
        "Recommended": (16, 14),
        "Baseline 1": (-8, -18),
        "Baseline 2": (10, -10),
        "Baseline 3": (-22, 10),
    }

    legend_handles = []
    for _, row in df.iterrows():
        method = str(row["method"])
        style = _style_for(method)

        x = float(row["E2_quality_weighted"])
        y = float(row["F1_gini"])

        ax.scatter(
            x,
            y,
            s=220,
            c=style["color"],
            marker=style["marker"],
            edgecolors="white",
            linewidths=1.2,
            zorder=4,
        )

        key = next((k for k in label_offsets.keys() if method.startswith(k)), "Recommended")
        dx, dy = label_offsets.get(key, (14, 14))
        ax.annotate(
            f"E1={row['E1_total_service']:.0f}\nE2={row['E2_quality_weighted']:.0f}\nGini={row['F1_gini']:.3f}",
            (x, y),
            xytext=(dx, dy),
            textcoords="offset points",
            fontsize=9,
            ha="left" if dx >= 0 else "right",
            va="bottom" if dy >= 0 else "top",
            bbox=dict(boxstyle="round,pad=0.28", facecolor="#f3f4f6", edgecolor=TU["gray"], alpha=0.96),
            arrowprops=dict(arrowstyle="->", color=TU["gray"], lw=1.0, shrinkA=6, shrinkB=6),
            zorder=6,
        )

        legend_handles.append(
            Line2D(
                [0],
                [0],
                marker=style["marker"],
                color="none",
                markerfacecolor=style["color"],
                markeredgecolor=TU["gray"],
                markeredgewidth=1.0,
                markersize=11,
                label=method,
            )
        )

    # 添加权衡箭头
    ax.annotate('', xy=(135000, 0.05), xytext=(105000, 0.30),
                arrowprops=dict(arrowstyle='<->', color=TU["mauve"], lw=2))
    ax.text(115000, 0.20, 'Efficiency-Fairness\nTradeoff', fontsize=10, ha='center',
            color=TU["mauve"], style='italic', bbox=dict(facecolor='white', edgecolor='none', alpha=0.8, pad=2), zorder=10)

    ax.set_xlabel('E2 (Quality-Weighted Service Volume)', fontsize=12)
    ax.set_ylabel('F1 (Gini Coefficient, lower = fairer)', fontsize=12)
    # ax.set_title('Fig.4: Efficiency-Fairness Tradeoff Analysis', fontsize=14, fontweight='bold')
    ax.legend(
        handles=legend_handles,
        loc="upper left",
        fontsize=9.5,
        labelspacing=0.6,
        borderpad=0.6,
        handletextpad=0.6,
        framealpha=0.92,
    )
    style_axes(ax)

    # 设置轴范围
    ax.set_xlim(95000, 140000)
    ax.set_ylim(0, 0.40)

    plt.tight_layout()
    plt.savefig(FIGURES_PATH / 'fig4_efficiency_fairness.png', dpi=150, bbox_inches='tight')
    plt.close()


def fig5_calendar_heatmap():
    """Fig.5: 日历热力图"""
    print("  生成 Fig.5: 日历热力图...")

    df_calendar = pd.read_excel(SCHEDULE_PATH, sheet_name='calendar')
    df_allocate = pd.read_excel(ALLOCATE_PATH)

    # 创建站点μ映射
    mu_map = dict(zip(df_allocate['site_id'], df_allocate['mu']))

    # 计算每天的总需求
    daily_demand = []
    for _, row in df_calendar.iterrows():
        demand = 0
        if pd.notna(row['site_1_id']):
            demand += mu_map.get(int(row['site_1_id']), 0)
        if pd.notna(row['site_2_id']):
            demand += mu_map.get(int(row['site_2_id']), 0)
        daily_demand.append(demand)

    df_calendar['total_demand'] = daily_demand

    # 创建12x31的热力图矩阵
    heatmap_data = np.full((12, 31), np.nan)

    for _, row in df_calendar.iterrows():
        day = row['day']
        # 简单映射: 假设每月30/31天
        month = (day - 1) // 31
        day_of_month = (day - 1) % 31
        if month < 12:
            heatmap_data[month, day_of_month] = row['total_demand']

    fig, ax = plt.subplots(figsize=(14, 8))

    im = ax.imshow(heatmap_data, cmap=_cmap_heat(), aspect='auto', interpolation='nearest')

    # 颜色条
    cbar = plt.colorbar(im, ax=ax, shrink=0.8)
    cbar.set_label('Daily Total Demand (μ₁ + μ₂)', fontsize=11, color=TU["text"])

    # 轴标签
    ax.set_xticks(np.arange(31))
    ax.set_xticklabels(np.arange(1, 32), fontsize=8)
    ax.set_yticks(np.arange(12))
    ax.set_yticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
                        'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'], fontsize=10)

    ax.set_xlabel('Day of Month', fontsize=12)
    ax.set_ylabel('Month', fontsize=12)
    # ax.set_title('Fig.5: Annual Schedule Calendar Heatmap (Daily Demand)', fontsize=14, fontweight='bold')
    ax.grid(False)

    plt.tight_layout()
    plt.savefig(FIGURES_PATH / 'fig5_calendar_heatmap.png', dpi=150, bbox_inches='tight')
    plt.close()


def fig6_gap_boxplot():
    """Fig.6: 访问间隔箱线图"""
    print("  生成 Fig.6: 访问间隔箱线图...")

    df_gaps = pd.read_excel(SCHEDULE_PATH, sheet_name='gap_statistics')

    # 过滤有效数据
    df_valid = df_gaps[df_gaps['gap_mean'].notna()].copy()

    # 按k分组
    df_valid['k_group'] = pd.cut(df_valid['k'], bins=[0, 5, 10, 15, 20, 40],
                                  labels=['1-5', '6-10', '11-15', '16-20', '21+'])

    fig, axes = plt.subplots(1, 2, figsize=(14, 6))

    # 左图: 间隔均值按k分组的箱线图
    ax1 = axes[0]
    groups = df_valid.groupby('k_group')['gap_mean'].apply(list).values
    group_labels = ['1-5', '6-10', '11-15', '16-20', '21+']

    bp = ax1.boxplot([g for g in groups if len(g) > 0], labels=group_labels[:len(groups)],
                     patch_artist=True)

    colors = _cmap_k()(np.linspace(0.2, 0.9, len(groups)))
    for patch, color in zip(bp['boxes'], colors):
        patch.set_facecolor(color)
        patch.set_edgecolor("white")
        patch.set_linewidth(0.8)

    ax1.set_xlabel('Visit Frequency Group (k)', fontsize=12)
    ax1.set_ylabel('Mean Gap (days)', fontsize=12)
    ax1.set_title('(a) Mean Visit Interval by Frequency Group', fontsize=12)
    style_axes(ax1)

    # 右图: 间隔CV的分布
    ax2 = axes[1]
    ax2.hist(df_valid['gap_cv'], bins=20, color=TU["blue_mid"], edgecolor="white", alpha=0.85)
    ax2.axvline(df_valid['gap_cv'].mean(), color=TU["mauve"], linestyle='--', linewidth=2,
                label=f'Mean CV = {df_valid["gap_cv"].mean():.3f}')
    ax2.axvline(df_valid['gap_cv'].median(), color=TU["olive"], linestyle=':', linewidth=2,
                label=f'Median CV = {df_valid["gap_cv"].median():.3f}')

    ax2.set_xlabel('Coefficient of Variation (CV) of Gaps', fontsize=12)
    ax2.set_ylabel('Number of Sites', fontsize=12)
    ax2.set_title('(b) Distribution of Gap Regularity (CV)', fontsize=12)
    ax2.legend(fontsize=10)
    style_axes(ax2)

    # plt.suptitle('Fig.6: Visit Interval Analysis', fontsize=14, fontweight='bold', y=1.02)
    plt.tight_layout()
    plt.savefig(FIGURES_PATH / 'fig6_gap_boxplot.png', dpi=150, bbox_inches='tight')
    plt.close()


def fig7_sensitivity():
    """Fig.7: 敏感性分析"""
    print("  生成 Fig.7: 敏感性分析...")

    # 读取敏感性分析结果
    df_C = pd.read_excel(SENSITIVITY_PATH, sheet_name='sensitivity_C')
    df_p = pd.read_excel(SENSITIVITY_PATH, sheet_name='sensitivity_p_thresh')
    df_cbar = pd.read_excel(SENSITIVITY_PATH, sheet_name='sensitivity_c_bar')
    df_base = pd.read_excel(SENSITIVITY_PATH, sheet_name='baseline').iloc[0]
    base_C = int(df_base['C'])
    base_p_thresh = float(df_base['p_thresh'])
    base_c_bar = float(df_base['c_bar'])
    base_E1 = float(df_base['E1'])
    base_E2 = float(df_base['E2'])

    fig, axes = plt.subplots(2, 2, figsize=(14, 10))

    # (a) C对E1的影响
    ax1 = axes[0, 0]
    ax1.plot(df_C['C'], df_C['E1'], 'o-', color=TU["blue_dark"], linewidth=2, markersize=7)
    ax1.axhline(base_E1, color=TU["taupe"], linestyle='--', alpha=0.9, label=f'Baseline (C={base_C}, p={base_p_thresh:g})')
    ax1.set_xlabel('Effective Capacity (C)', fontsize=11)
    ax1.set_ylabel('E1 (Total Service Volume)', fontsize=11)
    ax1.set_title('(a) Effect of C on E1', fontsize=12)
    ax1.legend(fontsize=9)
    style_axes(ax1)

    # (b) C对修正站点数的影响
    ax2 = axes[0, 1]
    ax2.bar(df_C['C'].astype(str), df_C['n_corrected'], color=TU["green"], edgecolor="white", alpha=0.9, linewidth=0.7)
    ax2.set_xlabel('Effective Capacity (C)', fontsize=11)
    ax2.set_ylabel('Number of Corrected Sites', fontsize=11)
    ax2.set_title('(b) Effect of C on Correction Count', fontsize=12)
    style_axes(ax2, grid_axis="y")

    # (c) p_thresh对指标的影响
    ax3 = axes[1, 0]
    ax3.plot(df_p['p_thresh'], df_p['E1'], 'o-', color=TU["teal"], linewidth=2, markersize=7, label='E1')
    ax3.set_xlabel('Truncation Threshold (p_thresh)', fontsize=11)
    ax3.set_ylabel('E1 (Total Service Volume)', fontsize=11)
    ax3.set_title('(c) Effect of p_thresh on E1', fontsize=12)
    ax3.legend(fontsize=9)
    style_axes(ax3)

    # (d) c_bar对E2的影响
    ax4 = axes[1, 1]
    ax4.plot(df_cbar['c_bar'], df_cbar['E2'], 's-', color=TU["mauve"], linewidth=2, markersize=7, label='E2')
    ax4.axhline(base_E2, color=TU["taupe"], linestyle='--', alpha=0.9, label=f'Baseline (c̄={base_c_bar:g})')
    ax4.set_xlabel('Quality Threshold (c̄)', fontsize=11)
    ax4.set_ylabel('E2 (Quality-Weighted Service)', fontsize=11)
    ax4.set_title('(d) Effect of c̄ on E2', fontsize=12)
    ax4.legend(fontsize=9)
    style_axes(ax4)

    # plt.suptitle('Fig.7: Sensitivity Analysis of Model Parameters', fontsize=14, fontweight='bold', y=1.02)
    plt.tight_layout()
    plt.savefig(FIGURES_PATH / 'fig7_sensitivity.png', dpi=150, bbox_inches='tight')
    plt.close()


def main():
    print("=" * 60)
    print("Step 09: 可视化")
    print("=" * 60)

    print(f"\n输出目录: {FIGURES_PATH}")

    # 生成所有图表
    print("\n[1] 生成图表...")
    apply_tu_theme()

    js_path = export_fig1_points_js()
    print(f"  已更新交互地图数据: {js_path.name}")

    fig1_site_map()
    fig2_demand_correction()
    fig3_k_distribution()
    fig4_efficiency_fairness()
    fig5_calendar_heatmap()
    fig6_gap_boxplot()
    fig7_sensitivity()

    # 列出生成的文件
    print(f"\n[2] 已生成图表:")
    for f in sorted(FIGURES_PATH.glob('*.png')):
        print(f"    {f.name}")

    print("\n" + "=" * 60)
    print("Step 09 完成")
    print("=" * 60)


if __name__ == "__main__":
    main()
